Literature DB >> 29326115

Understanding variation in metabolic rate.

Amanda K Pettersen1, Dustin J Marshall2, Craig R White2.   

Abstract

Metabolic rate reflects an organism's capacity for growth, maintenance and reproduction, and is likely to be a target of selection. Physiologists have long sought to understand the causes and consequences of within-individual to among-species variation in metabolic rates - how metabolic rates relate to performance and how they should evolve. Traditionally, this has been viewed from a mechanistic perspective, relying primarily on hypothesis-driven approaches. A more agnostic, but ultimately more powerful tool for understanding the dynamics of phenotypic variation is through use of the breeder's equation, because variation in metabolic rate is likely to be a consequence of underlying microevolutionary processes. Here we show that metabolic rates are often significantly heritable, and are therefore free to evolve under selection. We note, however, that 'metabolic rate' is not a single trait: in addition to the obvious differences between metabolic levels (e.g. basal, resting, free-living, maximal), metabolic rate changes through ontogeny and in response to a range of extrinsic factors, and is therefore subject to multivariate constraint and selection. We emphasize three key advantages of studying metabolic rate within a quantitative genetics framework: its formalism, and its predictive and comparative power. We make several recommendations when applying a quantitative genetics framework: (i) measuring selection based on actual fitness, rather than proxies for fitness; (ii) considering the genetic covariances between metabolic rates throughout ontogeny; and (iii) estimating genetic covariances between metabolic rates and other traits. A quantitative genetics framework provides the means for quantifying the evolutionary potential of metabolic rate and why variance in metabolic rates within populations might be maintained.
© 2018. Published by The Company of Biologists Ltd.

Entities:  

Keywords:  Evolution; Metabolism; Quantitative genetics; Selection

Mesh:

Year:  2018        PMID: 29326115     DOI: 10.1242/jeb.166876

Source DB:  PubMed          Journal:  J Exp Biol        ISSN: 0022-0949            Impact factor:   3.312


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